Ideas around warden self-learning so even if AI gets improved we can still stay ahead of it

[solar-flare99]

Tier 1: Automated Feed Pipeline (low-hanging fruit)

The feed already has fetch → merge → sign. Just automate the trigger.

• GitHub Actions cron — daily NVD poll, auto-merge, auto-sign, auto-commit. The scripts exist (fetch_nvd_intel.py, merge_intel.py, sign_feed.sh) but no
  workflow wires them together yet.
• Dynamic keyword expansion — instead of hardcoded KEYWORDS = ["LangChain", "OpenAI", ...], pull keywords from the feed itself or a community-maintained
  list. New AI frameworks appear weekly; the keyword list goes stale fast.
• Multi-source ingestion — beyond NVD, pull from GitHub Security Advisories (GHSA), OSV.dev, or PyPI/npm advisory databases. Each source = one more
  fetch_*.py script feeding the same merge pipeline.

---

Tier 2: Feed-Carried Rules (the feed teaches Warden new tricks)

Instead of the feed only carrying advisories, have it carry detection patterns too.

• Extend the threat object schema with an optional patterns array. When an advisory includes patterns, Warden's policy engine loads them as dynamic
  rules at runtime — no YAML update needed.
• Example: a new CVE drops for langchain-experimental. The pipeline creates an advisory with "patterns": ["langchain.experimental.*unsafe"] and
  "affected": ["langchain-experimental<=0.0.50"]. Warden picks it up on next feed load.
• This makes the feed a rule distribution channel, not just a reference document.
• Signature verification ensures only trusted patterns get loaded (no injection via tampered feed).

---

Tier 3: Session-Based Learning (Warden teaches itself)

Use the session store (SQLite + JSONL) that already captures every tool call.

• Pattern mining — periodically analyze blocked/warned events across all sessions. If the same command structure keeps appearing but isn't covered by a
  rule, flag it as a candidate rule.
• False positive tracking — if a user allowlists something or the same warn gets dismissed N times, reduce confidence. Feed this back to rule severity.
• Evasion detection — if a command is structurally similar to a blocked command but passes (e.g., using backticks instead of $(...) for shell
  injection), propose a pattern refinement.
• This could be a warden learn command that reads session history and proposes new rules or pattern improvements.

---

Tier 4: Community Feedback Loop

Deployed agents report anonymized signal back to improve the feed.

• Opt-in telemetry — warden/telemetry.py collects anonymized finding categories (never commands/secrets). Reports which rules fire, which get
  allowlisted, false positive rates.
• GitHub Issues as threat input — the implementation.md already specifies process-community-intel.yml. Users file issues with a structured template →
  pipeline converts them to advisories.
• Rule effectiveness scoring — each rule gets a confidence score that adjusts based on real-world signal. Low-confidence rules become warn instead of
  block automatically.

---

Tier 5: LLM-Assisted Evolution (the agent improves itself)

Use Claude to analyze new threats and generate rules.

• When the NVD pipeline fetches a new CVE, pass the description through an LLM to generate candidate regex patterns for the policy engine. Human reviews
  before signing.
• When warden learn identifies a gap (unknown attack pattern), use an LLM to draft a YAML rule from the raw session event data.
• Skill scanner could use an LLM to analyze MCP server source code beyond regex — understanding intent, not just pattern matching (similar to what
  Socket does with their "AI-powered detection").

---

What's most interesting to you?

The tiers go from "just wire up what exists" (Tier 1) to "the system genuinely evolves its own detection" (Tier 5). They're also not mutually exclusive
— Tier 1 is a prerequisite for everything else.

[solar-flare99]

You are implementing a new feature in the PrismorSec/immunity-agent repo called
"scoped agent". Read the existing codebase carefully before writing any code.

## What it is

Today, Warden enforces a static policy.yaml at every tool-call hook. The problem:
static rules have to be broad enough to cover every possible task, so they either
miss novel attacks or block legitimate work.

Scoped agent fixes this by generating a minimal, task-specific rule set at session
start — before any external content enters the model's context — and enforcing it
alongside policy.yaml for the duration of that session only.

The active rule set becomes:  policy.yaml (base) + scoped_agent (session-only)

## Exactly what to build

### 1. New hook: UserPromptSubmit → session_start_handler

In the hook dispatcher (wherever PreToolUse / PostToolUse are registered),
add a new handler that fires on UserPromptSubmit — the very first hook Claude Code
provides, before any tool runs.

This handler should:
  a) Extract the user's task goal from the prompt text
  b) Call synthesize_scoped_rules(goal, available_tools) — see step 2
  c) Display the generated rules to the user and ask for confirmation
     (print to terminal, similar to how warden check outputs today)
  d) If confirmed, store the rules in a session-scoped dict (not written to disk,
     not merged into policy.yaml)
  e) If rejected, ask the user to edit and re-confirm, or skip scoped agent entirely

The confirmation prompt should look like:

  ┌─ scoped agent rules for this session ─────────────────────┐
  │  allowed_tools:   [Read, EmailDraft]                       │
  │  allowed_paths:   ["*.pdf", "~/Documents/**"]              │
  │  deny_tools:      [Bash, Write, GitCommand, WebSearch]     │
  │  deny_network:    all except smtp.gmail.com                │
  │                                                            │
  │  These rules apply this session only and are not saved.    │
  │  Confirm? [Y/n/edit]                                       │
  └────────────────────────────────────────────────────────────┘

### 2. New function: synthesize_scoped_rules(goal, available_tools)

Location: a new file at warden/scoped_agent.py

This function calls the Anthropic API (reuse whatever API client Cloak already
uses) with the following system prompt:

  """
  You are a security policy synthesizer for an AI coding agent.
  Given a task description and a list of available tools, output a minimal
  JSON object that scopes the agent to only what this task genuinely requires.
  Be conservative — if a tool is not clearly needed, exclude it.
  Output only valid JSON, no explanation, no markdown fences.
  Schema:
  {
    "allowed_tools": [...],    // tool names the task needs
    "allowed_paths": [...],    // glob patterns for file paths the task needs
    "deny_tools": [...],       // tools explicitly not needed (complement of allowed)
    "deny_network": "all" | ["domain1", "domain2"]  // allowed outbound hosts
  }
  """

User message:
  "Task: {goal}\nAvailable tools: {available_tools}"

Parse the JSON response. If parsing fails, return None and skip scoped agent
(fail open — don't block the user).

Model: claude-haiku-4-5-20251001  (fast, cheap, sufficient for rule synthesis)
Max tokens: 300
Temperature: 0

### 3. Extend PreToolUse enforcement to check scoped agent rules

In the existing PreToolUse hook handler, after the existing policy.yaml checks,
add a second check against the session-scoped rules (if they exist):

  def check_scoped_agent(tool_name, tool_args, scoped_rules):
      if scoped_rules is None:
          return ALLOW

      # Tool check
      if tool_name not in scoped_rules["allowed_tools"]:
          return BLOCK, f"[scoped agent] {tool_name} is not in scope for thi

[Ar9av]

I'd be careful here.

Self-learning sounds good until the system starts hardening around noise, one-off repo quirks, or a maintainer's temporary habits. Security rules that teach themselves can drift into a bad place fast if the feedback loop is weak.

The safer version is suggestion-first. Let Warden mine patterns, cluster near-misses, and propose candidate rules with evidence. Then a human promotes them. You still learn, but you do not let the guard rewrite itself in the dark.